Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat - Yang, Mengjiao; Hassan, Muhammad Adeel; Xu, Kaijie; Zheng, Chengyan; Rasheed, Awais; Zhang, Yong; Jin, Xiuliang; Xia, Xianchun; Xiao, Yonggui; He, Zhonghu (Frontiers in Plant Science, 2020)
Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0=0 mm, T1=80 mm, T2=120 mm and T3=160 mm), and four N (T0=0, T1=120 kg ha−1, T2=180 kg ha−1 and T3=240 kg ha−1) treatments respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalised difference vegetation index (RNDVI), green normalised difference vegetation index (GNDVI), normalised difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalised green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R2=0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R2=0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha−1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.